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Hi, dear author, setting grammar in Vosk is very useful, so I copy UpdateGrammarFst() to Kaldi and test it with open source chain model(http://kaldi-asr.org/models/m13), but the result is bad( I set faster_decode_opts_.beam = 1000, I change other decoders and models, also bad), the background noise was be recognized as words, and some extra words. For example:
The speaker said "magnets can be found on a can opener".
start end
WORDS: 0.02 0.03
WORDS: 0.12 0.24 can
WORDS: 0.24 0.52
WORDS: 1.4 2.06 magnets
WORDS: 2.06 2.33 can
WORDS: 2.33 2.45 be
WORDS: 2.45 2.5 a
WORDS: 2.5 2.93 found
WORDS: 3 3.19 on
WORDS: 3.19 3.26 a
WORDS: 3.26 3.64 can
WORDS: 3.64 4.2 opener
But I can get correct result in Vosk. Any solution to make model no such sensitive?
Or maybe setting grammar has existed in Kaldi, can you please give me some tips, Thanks!
The text was updated successfully, but these errors were encountered:
Hi, dear author, setting grammar in Vosk is very useful, so I copy UpdateGrammarFst() to Kaldi and test it with open source chain model(http://kaldi-asr.org/models/m13), but the result is bad( I set faster_decode_opts_.beam = 1000, I change other decoders and models, also bad), the background noise was be recognized as words, and some extra words. For example:
The speaker said "magnets can be found on a can opener".
start end
WORDS: 0.02 0.03
WORDS: 0.12 0.24 can
WORDS: 0.24 0.52
WORDS: 1.4 2.06 magnets
WORDS: 2.06 2.33 can
WORDS: 2.33 2.45 be
WORDS: 2.45 2.5 a
WORDS: 2.5 2.93 found
WORDS: 3 3.19 on
WORDS: 3.19 3.26 a
WORDS: 3.26 3.64 can
WORDS: 3.64 4.2 opener
But I can get correct result in Vosk. Any solution to make model no such sensitive?
Or maybe setting grammar has existed in Kaldi, can you please give me some tips, Thanks!
The text was updated successfully, but these errors were encountered: